Should we condition on the number of points when modelling spatial point patterns?

TL;DR

This paper examines the practice of conditioning on the number of points in spatial point pattern models, finding it can improve model validation and parameter estimation, though its benefits vary with different summary statistics.

Contribution

It investigates the effects of conditioning on the number of points in spatial point process modeling and validation, providing insights into when this approach is advantageous.

Findings

Conditioning can lead to narrower envelopes in validation tests.

It can correct conservativeness in hypothesis testing.

Benefits depend on the specific summary statistic used.

Abstract

We discuss the practice of directly or indirectly assuming a model for the number of points when modelling spatial point patterns even though it is rarely possible to validate such a model in practice since most point pattern data consist of only one pattern. We therefore explore the possibility to condition on the number of points instead when fitting and validating spatial point process models. In a simulation study with different popular spatial point process models, we consider model validation using global envelope tests based on functional summary statistics. We find that conditioning on the number of points will for some functional summary statistics lead to more narrow envelopes and that it can also be useful for correcting for some conservativeness in the tests when testing composite hypothesis. However, for other functional summary statistics, it makes little or no difference to condition on the number of points. When estimating parameters in popular spatial point process models, we conclude that for mathematical and computational reasons it is convenient to assume a distribution for the number of points.